# dtControl: Decision Tree Learning Algorithms for Controller Representation

arXiv: https://arxiv.org/abs/2002.04991 · HTML: https://mlsanity.com/paper/2002.04991/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts](https://mlsanity.com/paper/2101.07202/) (2021)
- [Optimal Decision Tree Policies for Markov Decision Processes](https://mlsanity.com/paper/2301.13185/) (2023)
- [Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces](https://mlsanity.com/paper/2208.12804/) (2022)
- [Bisimulation Learning](https://mlsanity.com/paper/2405.15723/) (2024)
- [Learning Explainable and Better Performing Representations of POMDP Strategies](https://mlsanity.com/paper/2401.07656/) (2024)
- [Analyzing Intentional Behavior in Autonomous Agents under Uncertainty](https://mlsanity.com/paper/2307.01532/) (2023)
- [In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search](https://mlsanity.com/paper/2409.03260/) (2024)
- [Explaining Control Policies through Predicate Decision Diagrams](https://mlsanity.com/paper/2503.06420/) (2025)
- [Uppaal Coshy: Automatic Synthesis of Compact Shields for Hybrid Systems](https://mlsanity.com/paper/2508.16345/) (2025)
- [Explainable Representation of Finite-Memory Policies for POMDPs using Decision Trees](https://mlsanity.com/paper/2411.13365/) (2024)
- [Resilient Strategies for Stochastic Systems: How Much Does It Take to Break a Winning Strategy?](https://mlsanity.com/paper/2602.24191/) (2026)
- [dtControl2+varepsilon: Trading Optimality for Explainability in MDPs via Decision Trees](https://mlsanity.com/paper/2607.25925/) (2026)

## Abstract (arXiv)

Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision trees are smaller and more explainable. We present dtControl, an easily extensible tool for representing memoryless controllers as decision trees. We give a comprehensive evaluation of various decision tree learning algorithms applied to 10 case studies arising out of correct-by-construction controller synthesis. These algorithms include two new techniques, one for using arbitrary linear binary classifiers in the decision tree learning, and one novel approach for determinizing controllers during the decision tree construction. In particular the latter turns out to be extremely efficient, yielding decision trees with a single-digit number of decision nodes on 5 of the case studies.